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Research Synthesis

 

Research Synthesis involves integration and synthesis of multiple studies and data sources to develop targeted insights.

 

Typically a research synthesis project would integrate information from different types of studies - qualitative, quantitative, syndicated and tracking studies, and possibly other sources of information such as competitive intelligence.

 

 

Framework

 

Our research synthesis framework consists of three components:

Description Our software-enabled research synthesis process uses a transparent, auditable and accessible methodology to transform research into high quality insights.

 

  • Datamodel
  • Methodology
  • Synthesis Process

 

These three components create a framework to resolve complex marketing problems into high quality actionable insights, through research synthesis.

 

This framework is enabled through InsightsBase system, which provides a software-based solution to synthesizing large volume of diverse types of information, in a manner that is transparent, auditable and accessible.

 

Syntheses using InsightsBase software are updatable. As new information comes in, it can be inputted into the synthesis datamodel. The synthesis output can then be re-evaluated in light of the new information.

 

Updatability makes synthesis dynamic rather than ad-hoc, point-in-time analysis. Thus research synthesis can reflect the changing nature of the marketplace and continue to provide valuable insights.

 

 

Data Model

Description Data Model is a multi-dimensional construct for synthesizing research. Each synthesis project has its own data model, that facilitates analysis across particular dimensions and measures.

A synthesis problem is first transformed into a Data Model that is mapped into the InsightsBase system.

 

As the synthesis problem typically has many dimensions (such as dimensions for classifying consumers like age, income, ethnicity, gender etc.), a Data Model is a multi-dimensional construct or a “cube” that enables analysis across particular dimensions and along the intersection of dimensions.

 

The Data Model also incorporates “measures” or metrics (such as awareness, frequency of purchase, etc.) that need to be analyzed along the dimensions described above.

 

Data Models provide for analysis of complex problems, dealing with multiple dimensions and multiple metrics.

 

Data Models are systematic transformations of research issues into a robust template for analyzing those issues. They facilitate processing knowledge at high levels of granularity, for example for micro-segments, enabling identification of granular opportunities and challenges, which are difficult to uncover through traditional analysis.

 

 

Methodology


We use Narrative Synthesis methodology for InsightsBase based synthesis:

 

  • Narrative synthesis refers to a process in which a narrative (as opposed to statistical) approach is used to synthesize findings extracted from multiple studies.
  • It deals with the findings and interpretations from research studies and other sources in their own terms, without any attempt to transform them into a common metric for analytical purposes.
  • This approach is flexible, allowing for different types of evidence – qualitative and quantitative, research and non-research – to be synthesized.
  • It moves beyond a summary of study findings to attempt a synthesis which can generate new insights or knowledge and be more systematic and transparent.

 

 
Process


The synthesis process starts with a definition of the issue and the analysis schema. These are then transformed into a Data Model which is mapped into the InsightsBase system.

 

Appropriate studies are identified that would serve as input for synthesis. Content analysis of these studies is performed to identify relevant content for synthesis. This content is then inputted into the InsightsBase system, within the Data Model designed above, through the application of metadata to the content.

 

Querying and aggregation of content is done within the analytical framework established at the beginning of the project. Analysis of this aggregated content is then performed to mine and codify the insights.

 

The robustness of synthesis is determined by the methodological quality of the included studies, the availability of the key information within these studies and the overall assessment of the strength of evidence available to support conclusions.

 

Conclusions and recommendations are stored within the InsightsBase system for easy access. The entire synthesis project thus resides within the InsightsBase system. The project can easily be updated with new information, and the earlier conclusions and recommendations re-examined to assess if the are still valid or should be updated, given the combination of existing information with the new information. A synthesis project can thus continue to provide insights based on the "totality of information" available within the company.


 

 

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